A novel parametric fuzzy CMAC network and its applications

A Control System Based on Auto-Fusion Cerebellar Perceptron Improved Model and Its Application to Consensus Problem

“…Previous research on the modeling of cerebellar functions includes a neural network model proposed by Albus (CMAC: Cerebral Model Articulation Controller) , known as the cerebellar perceptron. The advantages of this model are simple structure and fast learning .…”

System Identification Using Fuzzy Cerebellar Model Articulation Controllers

“…Many researchers have integrated the fuzzy concept into the CMAC network, such as in (Chen 2001;Dai et al, 2010;Guo et al, 2002;Jou, 1992;Ker et al, 1997;Lai & Wong, 2001;Lee & Lin, 2005;Lee et al, 2007a;Lee et al, 2007b;Lin & Lee, 2009;Peng & Lin, 2011;Wang, 1994;Zhang & Qian, 2000). In general, they use membership functions rather than basis functions, and the resulting structures are then called fuzzy CMACs (FCMACs).…”

“…In the following sections, being the example models in this chapter the self-constructing FCMAC (SC-FCMAC, Lee et al, 2007a) and the powerful parametric FCMAC (P-FCMAC, Lin & Lee, 2009) are reviewed, in order to provide readers the insight knowledge of how these FCMAC work. Companied by their corresponding architectures and learning schemes, illustrative examples of system identification are provided as well.…”

“…In this section the architecture and learning algorithms of the parametric FCMAC (P-FCMAC, Lin & Lee, 2009) are reviewed, which mainly derived from the traditional CMAC and Takagi-Sugeno-Kang (TSK) parametric fuzzy inference system (Sugeno & Kang, 1988;Takagi & Sugeno, 1985). Since the SCM are inherent in the scheme of input-space partition for the P-FCMAC model, the performance of P-FCMAC is definitional better than the SC-FCMAC.…”

“…With the advantages such as fast learning speed, high convergence rate, good generalization capability, and easier hardware implementation (Lin & Lee, 2009;Peng & Lin, 2011), the CMAC has been successfully applied to many fields; for example, identification (Lee et al, 2004), image coding (Iiguni, 1996), ultrasonic motors (Leu et al, 2010), grey relational analysis (Chang et al, 2010), pattern recognition (Glanz et al, 1991), robot control (Harmon et al, 2005;Mese, 2003;Miller et al, 1990), signal processing (Kolcz & Allinson, 1994), and diagnosis (Hung & Wang, 2004;Wang & Jiang, 2004). However, there are three main drawbacks of Albus' CMAC, i.e., larger required computing memory Leu et al, 2010;), relatively poor ability of function approximation (Commuri & Lewis, 1997;Guo et al, 2002;Ker et al, 1997), and difficulty of adaptively selecting structural parameters (Hwang & Lin, 1998;Lee et al, 2003).…”